The face of Non‐photosensitive trichothiodystrophy phenotypic spectrum: A subsequent study on paediatric population

Abstract Background Non‐photosensitive trichothiodystrophies (TTDs) are a diverse group of genodermatoses within the subset of conditions known as “sulphur‐deficient brittle hair” syndromes. A part of them has only recently been identified, revealing novel causative genes and very rare phenotypes of these genetic skin disorders. At the same time, the molecular basis of previously published and unresolved cases has been revealed through the introduction of innovative genetic techniques. We have previously described the facial phenotype of patients with the Photosensitive form of TTD during childhood. This study marks the beginning of an effort to expand the analysis to include individuals of the same age who do not have photosensitivity. Methods A total of 26 facial portraits of TTD paediatric patients with Non‐photosensitivity from the literature were analysed using computer‐aided technologies, and their facial features were examined through a detailed clinical review. Results Distinct facial features were identified in both Photosensitive and Non‐photosensitive TTDs. Conclusion The present study has comprehensively elucidated the facial features in TTDs, encompassing the Non‐photosensitive clinical spectrum.

T A B L E 1 Facial features of Non-photosensitive TTD patients in childhood.

TTD gene
No Following our previous study on Photosensitive phenotype (Pascolini et al., 2023), we analysed the facial characteristics of TTD patients with Non-photosensitivity during childhood to enhance and broaden our understanding of this unique topic.
For this purpose, we have selected all published paediatric patients within this clinical spectrum, detailing the facial features and analysing frontal portraits available with computer-aided technologies.This consecutive research is the first on patients with Non-photosensitive TTDs, illustrating interesting results and supporting our earlier evidence.

| SUBJECTS AND METHODS
We researched indexed articles in the online database PubMed (https:// pubmed.ncbi.nlm.nih.gov), focusing on patients aged 1-14 years, without any specific ethnicity preference, and with accessible frontal facial photographs.We used the following terms for the research: trichothiodystrophy, trichothiodystrophy 4, 5, 6, 7, 8, 9. Also, key words, such as BIDS, IBIDS, SIBIDS, Sabinas brittle hair, Pollitt and Amish Brittle Hair Brain syndromes (ABHBS), microcephaly developmental delay, and brittle hair syndrome (MDBH) were used for patient selection and identification of causative genes.From this initial phase, we identified a total of 34 patients.Of these, we excluded 2 patients because their age at the moment of the photo acquisition was not specified; 2 patients were excluded because the image could not be analysed using the CLINIC application of the Face2Gene platform (F2G, www.face2 gene.com) (vs21.5.0), powered by FDNA Inc., (Boston, USA), which we examined and represent a part of this study; 2 were excluded because there was no available information about their facial features, and their published portrait was not suitable for F2G; 2 of them were excluded because the TTD type (Photosensitive vs. Non-photosensitive) was not clear; and one more because the individual was 15 years old.
The images were then processed using the research application of F2G.With this tool, we performed a binary comparison analysis involving two experiments.We compared 26 images with those of 26 unaffected individuals matched for age and ethnicity, as well as with 22 Photosensitive patients from our previous study (Pascolini et al., 2023).
In the 2 DeepGestalt analyses (Non-photosensitive vs. Photosensitive; Non-photosensitive vs. Controls), the quality of separation was evaluated by creating composite images of both groups and measuring the area under the curve (AUC) of the receiver operating characteristic (ROC) curve (Figure 1a,b).
By applying the GestaltMatcher algorithm, we encoded a patient photo into a 320-dimensional vector, called facial phenotype descriptor (FPD), which can be interpreted as a coordinate in the clinical face phenotype space (CFPS).Distances between different FPDs in the CFPS define syndromic similarity-smaller distances indicate higher similarity.
By using GestaltMatcher, it is possible to classify syndromes, delineate new phenotypes, and cluster patients with similar clinical presentations.In the resulting pairwise comparison matrix (PCM), we compared the images of the target cohort cases (x and y axes) with the GestaltMatcher gallery (4300 images), which resulted in clustered similarity ranks.
Using a dimensionality reduction method to convert the 320-dimensional vectors to2 dimensions, allowed us to visualize them in a 2D scatterplot using t-SNE visualization.
Each data point corresponds to a patient photo.This visualization includes the target cohort and the top 3 most similar rare syndromes, each with at least 7 patient photos available in the GestaltMatcher database.The confidence ellipses have a radius of one standard deviation from each cluster's centroid.
The closer these data points are, the more similar their facial dysmorphology is.

| RESULTS
The present study cohort consists of 16 males and 10 females.The facial details shown in Table 1 will be used to describe the total number of individuals with such feature in this paragraph.Clinical terms have been listed in the table as originally reported.However, we added in square brackets the actually used craniofacial standard terminology (Allanson et al., 2009;Carey et al., 2009;Hall et al., 2009;Hennekam et al., 2009;Hunter et al., 2009).Thus, skull morphology was anomalous in 11 individuals, with 11 displaying various characteristics, including microcephaly (7), triangular face (2), dolichocephaly, prominent biparietal head, high forehead, broad forehead, and asymmetric face (each in 1).
Eyebrows were abnormal in 21 cases: they appeared sparse (9), short and sparse (3), marked reduced (2), absent (2) and in 1 (each) broken stubble and rarely visible; short, sparse and disordered; sparse lateral; sparse with absent lateral half; sparse, brittle, slow growing and absent outer.Eyelashes were involved in 20/21 and were sparse (8), marked reduced (2), short and sparse (2), absent (2), sparse and broken; very few intact; broken, sparse with absent lateral half; sparse, brittle and slow growing, each present in 1. Abnormal eyebrows and/or eyelashes were observed in all patients except one.
The oral region was involved in 7 cases: a high-arched palate was identifiable in 3, analogously to a wide mouth (including broad mouth and macrostomia); one individual had hypoplastic supraciliary/zygomatic arches and down-slanting mouth corners; a thick vermilion border of the lower lip and a long philtrum were observed in each case.Thirteen (13) patients exhibited ear anomalies: they were protruding in 4, simplified in 2, low-set, protruding with low-set, large in 1 (each).Nose malformations were ascertained in 5: abnormal nasal bridge (depressed, high) was registered in 2; beaked nose, short nose with large and depressed nasal root, thick alae, anteverted nares and thick nasal root were identifiable in 1 (each).
For 15 patients, information about their teeth was available.Eight out of the fifteen had impairment: they were crowded, small, and misaligned, with widely spaced, hypoplastic teeth, especially in the lower arch in 1 (each); in the same proportion, there was yellowish discolouration, a single fused lower central incisor, hypodontia, and multiple decays.Caries was diagnosed in 3 teeth.
The Non-photosensitive versus Photosensitive analysis showed a p-value of 0.093, whereas in the Nonphotosensitive TTD versus Controls experiment, this parameter resulted in <0.0001 (Figure 1a,b).
In the PCM of the GestaltMatcher analysis, the number in each box represents the facial phenotype similarity rank achieved (Figure S1).Dark green values (indicating a low rank) in this matrix indicate higher similarity in facial phenotypic features.For example, if the case IDs are the same on the x and y axes, the value is 0. This value represents the similarity rank achieved when each photo is compared to itself.In addition, a clustering method was applied to the matrix to group similar ranks together.If we look at the PMC, and we take the top level that splits the cohort in two, we see that the majority of the case IDs on the right belong to the Non-photosensitive TTD group, whereas the majority of the case IDs on the left belong to the Photosensitive TTD group, but there is still some overlap (Figure S1).
In the t-SNE representation, Figure 2 shows that both subgroups clusters are separate from the top 3 most similar syndromes in the GestaltMatcher database: Noonan (NS), Fragile X (FXS) and Ectodermal Dysplasia 1 (Hypohidrotic, X-linked; XHED) syndromes.They do not seem to separate among themselves, indicating they are likely quite similar in terms of facial phenotype (Figure 2).

| DISCUSSION
This represents a follow-up study to one we recently published on the facial phenotype of paediatric patients with Photosensitive TTDs.In this second research, we have considered the Non-photosensitive clinical spectrum, a wide genetically determined group of TTDs.In recent years, the introduction of novel molecular techniques has enabled us to identify the underlying cause of previously described patients and additional individuals, as well as discover new genetic determinants.Based on the molecular defect, four Non-photosensitive TTDs can be identified: the genes involved have diverse functions and drive distinct molecular pathways, highlighting the strong genetic heterogeneity of this group of conditions.MPLKIP, which causes TTD4, is the most prevalent gene in this cohort (10/26).It encodes an M-phase specific PLK1-interacting protein, involved in the regulation of mitosis and cytokinesis (Zhang et al., 2007), as well as in RNA processing (Theil et al., 2023).The other two proteins, RNF113A (Ring Finger Protein 113A) and GTF2E2 (General Transcription Factor IIE Subunit 2), are less common and are respectively associated with TTD5 and TTD6.The first point of interest is the unique TTD gene located on chromosome X, which is a component of the spliceosome (Haselbach et al., 2018;Zhang et al., 2018) and plays a crucial role in the DNA repair process (Brickner et al., 2017).Meanwhile, GTF2E2 is crucial for promoter clearance by RNA polymerase and transcription activation (Peterson et al., 1991).However, the present study did not include patients with variants in the recently identified genes AARS1 (Alanyl-tRNA synthetase 1) and MARS1 (Methionyl-tRNA synthetase 1), causing TTD8 and TTD9 (Botta et al., 2021).This is because in the first case, we did not find any paper with images of children with MARS1 mutations (to the best of our knowledge), whereas in the only paper reporting a paediatric individual with an AARS1 variant, the age of the patient in the facial portrait was not clear (Botta et al., 2021).We also did not include the patient mentioned in Jackson et al. (1974) for the same reason, even though he represented one of the first clinical descriptions of a TTD4 patient in the Amish population, who identified the ABHS.In addition, information about his facial characteristics was not provided, and the image was not analysable with the CLINIC application of F2G.This also occurred with the two patients studied by King et al. (1984), who were diagnosed with TTD4 but were also noted to have photosensitivity, creating a source of confusion.This supported their exclusion from our earlier study.Moreover, their portraits were not suitable for the DeepGestalt analysis.Finally, we did not find facial photos of paediatric patients with variants in TARS1 (Threonyl-tRNA synthetase 1) and CARS1 (Cysteinyl-tRNA synthetase 1), associated respectively with TTD7 (Theil et al., 2019) and MDBH (Kuo et al., 2019).
The facial phenotype of the Non-photosensitive clinical spectrum in childhood has been analysed in the present study.This involved compiling the clinical details of previously published patients (from 1976 to 2020) and conducting additional experiments using the novel DeepGestalt and GestaltMatcher technologies.
The data we have obtained (Table 1) are consistent with our previous study, identifying skull anomalies, eye problems, oral defects, abnormal ear, nose, chin, and cheeks as the most prevalent facial features (100%).This is followed by eyebrows and eyelashes (95%) and teeth involvement (53%).This can lead to the recognition of a distinctive facial phenotype in TTD patients, suggesting a recurring pattern of facial abnormalities.A careful observation of these clinical features would be desirable in the diagnostic process.
This was also supported by the binary comparison analysis, which identified a different facial morphology between Non-photosensitive TTDs and controls, highlighting the good performance of the F2G platform in differentiating the two facial profiles.An overlap between the two main types of TTDs in the second experiment (Non-photosensitive vs. Photosensitive) is demonstrated by the p-value, which was not significant, indicating that both groups are not separable and then that their facial phenotype is similar (Figure 1a,b).
Notably, the GestaltMatcher algorithm revealed two similar syndromes that were not identified in the previous study (Figure 2), including XHED and NS.Except for the first disorder, which can be easily understood as a similar condition due to being a genodermatosis with impairment of ectodermal derivatives, it would be interesting to further explore the similarities with the other disorder, which is not classically considered a genetic disease primarily affecting the skin.Potential clinical intersection points can be represented by some dermatological signs identifiable in NS (woolly-like hair, low posterior hairline, ulerythema ophryogenes).If this is associated with a possible interaction between the underlying molecular pathways, it would be clarified.
Interestingly, XHED overlaps with both TTDs, whereas FXS and NS respectively with the Non-photosensitive and Photosensitive type (Figure 2).This could be considered for a correct differential diagnosis.
In conclusion, we characterized the facial phenotype of children within the Non-photosensitive TTD spectrum, building upon and expanding our previous study.
TTDs in their two main clinical forms, are characterized by a distinctive facial appearance in paediatric patients.

F
I G U R E 1 DeepGestalt analysis results.(a) Non-photosensitive versus Photosensitive and (b) Non-photosensitive versus Controls experiments.Score distribution and ROC curve, showing the comparison results, are provided (AUC = 0.744, p-value 0.093 and AUC = 0.950, p-value < 0.0001, respectively).

F
I G U R E 2 Results of the GestaltMatcher experiment.t-SNE visualization showing Photosensitive (in violet), Non-photosensitive (in red) TTDs and the most similar syndromes when compared using the GestaltMatcher database: Ectodermal Dysplasia 1 (in green), Noonan (in blue) and Fragile X (in orange) syndromes.

TTD gene No. Reference Individual Clinical classification/ variant Age Gender Skull Eye
Note:The terms are reported as in the original descriptions.Some terms have been indicated according to the American Journal of Medical Genetics (AJMG) standard terminology (Elements of Morphology, 2009) (in square brackets).Abbreviations: BIDS, brittle hair, intellectual impairment, decreased fertility, short stature; F, female; F2G ID, Face2Gene identity; IBIDS, ichthyosis, brittle hair and nails, intellectual impairment, short stature; M, male; m, months; na, not available; SIBIDS, osteosclerosis, ichthyosis, brittle hair, impaired intelligence, decreased fertility, short stature.